1. Technical Field
The present disclosure relates to imaging, and more particularly to a system and method for multimodal imaging for research assessment and diagnosis.
2. Description of Related Art
Near-infrared (NIR) diffuse optical tomography (DOT) relies on functional processes, and provides unique measurable parameters with potential to enhance breast tumor detection sensitivity and specificity. For example, several groups have demonstrated the feasibility of breast tumor characterization based on total hemoglobin concentration, blood oxygen saturation, water and lipid concentration and scattering.
The functional information derived with DOT is complementary to structural and functional information available to conventional imaging modalities such as magnetic resonance imaging MRI, x-ray mammography, and ultrasound. Thus the combination of functional data from DOT with structural/anatomical data from other imaging modalities holds potential for enhancing tumor detection sensitivity and specificity. To achieve this goal of data fusion, two general approaches can be employed. The first, concurrent imaging, physically integrates the DOT system into the conventional imaging instrument. This approach derives images in the same geometry and at the same time. The second approach, non-concurrent imaging, employs optimized stand-alone DOT devices to produce 3-D images that must then be combined with those of the conventional imaging modalities via software techniques. In this case, the images are obtained at different times and often in different geometries.
Few DOT systems have been physically integrated into conventional imaging modalities such as MRI, 18-22 x-ray mammography, and ultrasound for concurrent measurements. By doing so, however, these DOT systems can be limited by the requirements of the “other” imaging modality, for example, restrictions on metallic instrumentation for MRI, hard breast compression for x-ray mammography, limited optode combinations for ultrasound and MRI, x-ray, and time constraints. On the other hand, among the standalone DOT systems available today, few attempts have been made to quantitatively compare DOT images of the same breast cancer patient to those of other imaging modalities obtained at different times, because the non-concurrent co-registration problem presents many challenges.
Therefore, a need exists for quantitative and systematic methods for data fusion that utilize the high-quality data and versatility of the stand-alone imaging systems.